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In free markets workers and firms exchange work for salaries and they have both competing and mutual interests. Workers need to work to get a salary but they are interested in getting as high a salary as possible. Firms need to hire workers but they are interested in paying them as low a salary as possible. At the same time both categories need each other.

How large the non-observed economy (NOE) is and what determines its size in different countries and regions of the world is a question that has been and continues to be much studied by many observers (, ). The size of this sector in an economy has important ramifications. One is that it negatively affects the ability of a nation to collect taxes to support its public sector. The inability to provide public services can in turn lead more economic agents to move into the non-observed sector (). When such a sector is associated with criminal or corrupt activities it may undermine social capital and broader social cohesion (), which in turn may damage economic growth (; ). Furthermore, as international aid programs are tied to official measures of the size of economies, these can be distorted by wide variations in the relative sizes of the NOE across different countries, especially among the developing economies.

Forecasting is a key activity for policy makers. Given the possible complexity of the processes underlying policy targets, such as inflation, output gaps, or employment, and the difficulty of forecasting in real time, recourse is often taken to simple models. A dominant feature of such models is their linearity. However, recent evidence suggests that simple, though non-linear, models may be at least as competitive as linear ones for forecasting macro variables.

There is one main theme and correspondingly one formal result in this paper. On the basis of a general characterization of what is formally meant by a ‘complex economy’, underpinned by imaginative suggestions to this end in Foley (2003) and in Brock and Colander (2000; henceforth BC), it will be shown that an theory of economic policy is impossible for such an economy. There is, in addition, also a half-baked conjecture; it will be suggested, seemingly paradoxically, that a ‘complex economy’ can be formally based on the foundations of orthodox general equilibrium theory and, hence, a similar impossibility result is valid in this case, too.

The industrial dynamic literature has shown the existence of some stylized facts regarding firms’ distribution among which a very important one, for the reasons discussed in the paper, is the scaling law of firms’ size ().

In the recent literature on optimal monetary policy, policymakers are assumed to know the true model of the economy and observe accurately all relevant variables. The sources and properties of economic disturbance are also taken to be known. Uncertainty in this case arises only due to the unknown future realisations of these disturbances. In this context, “uncertainty” means the realisation of an event whose true probability distribution is known. Pure uncertainty, where the state space of outcomes is known but one is unable to assign probabilities, has largely been ignored. In practice, the policymaker’s choice is made in the face of tremendous uncertainty about the true structure of the economy, the impact policy actions have on the economy, and even about the current state of the economy. The policymaker is therefore unsure about his model, in the sense that there is a group of approximate models that he also considers as possibly true. Because uncertainty is pervasive, it is important to understand how alternative policies work when the policymaker cannot accurately observe important macro variables or when he employs a model of the economy that is incorrect in unknown ways.